PySpark - 无法显示随机森林模型的预测（无法执行用户定义的函数（$anonfun$1: (vector) => vector)）

Posted 2023-04-14

【中文标题】PySpark - 无法显示随机森林模型的预测（无法执行用户定义的函数（$anonfun$1: (vector) => vector)）

【英文标题】：PySpark - Impossible to show predictions of a random forest model (Failed to execute user defined function($anonfun$1: (vector) => vector))

【发布时间】：2019-09-20 16:42:38

【问题描述】：

我正在使用 PySpark（Python 3.5.2 和 Spark 2.2.0.2.6.4.0-91），并且我有一个预测值的数据框（通过使用 MLlib 库定义的随机森林模型），其结构如下：

DataFrame[id: bigint, features: vector, rawPrediction: vector, probability: vector, prediction: double]

我得到了它：

rf_predictions = random_forest_model.transform(dataframe)

但是当我想显示它的内容时，它只适用于前 2 列“id”和“features”：

rf_predictions.select("id","features").show()

但是当我尝试时：

rf_predictions.select("prediction").show()

为了显示“预测”列（与“原始预测”或“概率”列相同的问题），它返回给我以下错误：

    19/09/20 18:33:31 WARN TaskSetManager: Lost task 0.0 in stage 51.0 (TID 169, slmupd5hsn03.zres.ztech, executor 1): org.apache.spark.SparkException: Failed to execute user defined function($anonfun$1: (vector) => vector)
        at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
        at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
        at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
        at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234)
        at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228)
        at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
        at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
        at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
        at org.apache.spark.scheduler.Task.run(Task.scala:108)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
        at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.ArrayIndexOutOfBoundsException

19/09/20 18:33:32 ERROR TaskSetManager: Task 0 in stage 51.0 failed 4 times; aborting job
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/hdp/current/spark2-client/python/pyspark/sql/dataframe.py", line 336, in show
    print(self._jdf.showString(n, 20))
  File "/usr/hdp/current/spark2-client/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
  File "/usr/hdp/current/spark2-client/python/pyspark/sql/utils.py", line 63, in deco
    return f(*a, **kw)
  File "/usr/hdp/current/spark2-client/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o1887.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 51.0 failed 4 times, most recent failure: Lost task 0.3 in stage 51.0 (TID 172, slmupd5hsn01.zres.ztech, executor 2): org.apache.spark.SparkException: Failed to execute user defined function($anonfun$1: (vector) => vector)
        at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
        at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
        at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
        at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234)
        at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228)
        at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
        at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
        at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
        at org.apache.spark.scheduler.Task.run(Task.scala:108)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
        at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.ArrayIndexOutOfBoundsException

Driver stacktrace:
        at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1517)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1505)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1504)
        at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
        at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
        at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1504)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
        at scala.Option.foreach(Option.scala:257)
        at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1732)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1687)
        at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1676)
        at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
        at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2029)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2050)
        at org.apache.spark.SparkContext.runJob(SparkContext.scala:2069)
        at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:336)
        at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
        at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:2861)
        at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2150)
        at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2150)
        at org.apache.spark.sql.Dataset$$anonfun$55.apply(Dataset.scala:2842)
        at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65)
        at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2841)
        at org.apache.spark.sql.Dataset.head(Dataset.scala:2150)
        at org.apache.spark.sql.Dataset.take(Dataset.scala:2363)
        at org.apache.spark.sql.Dataset.showString(Dataset.scala:241)
        at sun.reflect.GeneratedMethodAccessor69.invoke(Unknown Source)
        at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
        at java.lang.reflect.Method.invoke(Method.java:498)
        at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
        at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
        at py4j.Gateway.invoke(Gateway.java:280)
        at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
        at py4j.commands.CallCommand.execute(CallCommand.java:79)
        at py4j.GatewayConnection.run(GatewayConnection.java:214)
        at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.spark.SparkException: Failed to execute user defined function($anonfun$1: (vector) => vector)
        at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
        at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
        at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
        at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234)
        at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228)
        at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
        at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
        at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
        at org.apache.spark.scheduler.Task.run(Task.scala:108)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
        ... 1 more
Caused by: java.lang.ArrayIndexOutOfBoundsException

然而，正如你所见，我没有使用任何 UDF 函数。你知道我为什么会得到这个错误或者我可以如何避免/修复它吗？

您是否认为有一种方法可以将此列转换为 RDD、List 或其他任何内容，然后将其重新构建为数据框列以便能够获得预测的标签？

非常感谢您。

最好的问候

【问题讨论】：

你能展示你如何适应你的随机森林的代码吗？从您的代码来看，您更像是想使用 spark.ml 模型而不是 mllib 模型

【参考方案1】：

试试

rf_predictions.select("rawPrediction").show()

或将 rawPrediction 映射到预测使用 Pipeline 尝试将 Pipeline 添加到您的代码中：

from pyspark.ml import Pipeline
# Chain indexers and forest in a Pipeline
# Train a RandomForest model.
rf = RandomForestClassifier(labelCol="", featuresCol="", numTrees=10)
pipeline = Pipeline(stages=[labelConverter])

# Train model.  This also runs the indexers.
model = pipeline.fit(dataframe)

# Make predictions.
predictions = model.transform(testData)